E2Pano：学习从事件数据到全景图像的重建
E2Pano: Learning Event-to-Panorama Image Reconstruction
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中文总结 AI 辅助
提出几何引导的事件到全景图像重建框架E2Pano，结合球面Transformer与频域监督，构建含4370个合成及30个真实场景的PanoScan数据集，实现高质量低计算成本的全景图像重建。
中文摘要 AI 辅助
事件相机提供微秒级时间分辨率和高动态范围，有望通过快速旋转扫描实现无运动模糊的全景成像。然而，现有基于优化的方法计算成本高昂，而此前基于学习的重建方法大多针对透视图像设计，缺乏对全景输出的几何感知支持。我们提出E2Pano，这是一个几何引导的事件到全景图像流水线，包含端到端可学习的光度重建阶段。我们的框架在整个流水线中保留几何映射得到的真实球面坐标，采用带有频域监督的轻量增强模块缩小事件-图像域差距，并利用带有3D位置嵌入的球面Transformer进行光度重建。在合成数据和采集的旋转扫描上的实验表明，与基于优化的基线相比，我们的方法重建质量更高、光度重建成本更低，尽管仅在合成数据上训练，在我们的采集协议下对真实采集数据的迁移效果也令人鼓舞。此外，我们构建了PanoScan数据集，包含4370个合成全景场景和30个真实世界全景场景，每个场景都配有对应的事件流。我们的数据集和代码将公开。
英文摘要
Event cameras offer microsecond-level temporal resolution and high dynamic range, potentially facilitating motion-blur-free panoramic imaging from fast rotational scanning. Nonetheless, existing optimization-based methods remain computationally demanding, while prior learning-based reconstruction methods are largely designed for perspective imagery and lack geometry-aware support for panoramic outputs. We present E2Pano, a geometry-guided event-to-panorama pipeline with an end-to-end learnable photometric reconstruction stage. Our framework preserves real spherical coordinates from geometric mapping throughout the pipeline, employs a lightweight enhancement module with frequency-domain supervision to bridge the event-image domain gap, and leverages a spherical Transformer with 3D positional embeddings for photometric reconstruction. Experiments on synthetic data and captured rotational scans show improved reconstruction quality and lower photometric reconstruction cost than optimization-based baselines, together with encouraging transfer to real captures under our acquisition protocol despite training purely on synthetic data. Additionally, we construct PanoScan, a dataset with 4,370 synthetic and 30 real-world panoramic scenes paired with event streams. Our dataset and code will be released.
发表机构
- The University of Hong Kong（香港大学）
机构由 AI 辅助整理，请以论文原文为准。
